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考虑换道切入安全的混行车辆队列控制方法

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智能网联车辆以队列行驶可以大幅提升道路的通行能力,但也会带来新的道路交通安全问题.针对随机车辆换道切入引发的碰撞风险,提出一种基于深度强化学习的混行车辆队列分布式控制方法.首先,针对车辆换道切入场景特点,考虑多控制目标建立深度强化学习奖励和智能体网络结构,建立混行车辆队列分层控制模型;其次,结合多因素道路安全性评价指标,利用复频域方法分析控制系统稳定性,得到车辆队列行驶安全性约束条件;最后,与线性控制和模型预测控制方法进行Prescan/Simulink联合仿真对比验证,相同条件下考虑换道切入安全的混行车辆队列控制方法使车间距降幅减小56.9％和52.1％,速度降幅减小58.72％和51.19％,车速标准差峰值降低54.8％和49％,碰撞时间最小值提高420.7％和265％,表明该方法应用于混行车辆队列行驶可大幅提升道路安全性.
Mixed Vehicular Platoon Control Method Considering the Safety of Lane-Change and Cut-In
Intelligent and connected vehicles driving in platoon can significantly improve road capacity,but it also causes new road traffic safety risks.Aiming at the collision risk caused by random vehicular lane-change and cut-in,this paper proposes a novel distributed control method for the mixed vehicular platoon based on deep reinforcement learning.Firstly,according to the characteristics of vehicle lane-change and cut-in scenarios,the deep reinforcement learning reward and agent network structure were designed with multiple control objectives,and a hierarchical control model for mixed vehicular platoon was designed.Secondly,considering the evaluation indexes of road safety,the stability of the control system was analyzed by using the complex frequency domain method,and the final safety constraints for vehicular platoon can be obtained.Finally,compared with traditional linear control and model predictive control method based on Prescan/Simulink co-simulation experiments,it was found that under the proposed method in the same scenarios,the spacing descend has been reduced by 56.9％and 52.1％,the speed descend has been reduced by 58.72％and 51.19％,the maximum standard deviation of speed has been reduced by 54.8％and 49％,and the minimum time-to-collision has been improved by 420.7％and 265％.It is demonstrated that the proposed method applied in mixed vehicular platoon control can significantly improve road safety.

intelligent trafficroad traffic safetyintelligent and connected vehiclesvehicular platoon operationvehicle lane-change and cut-indeep reinforcement learningstring stability

王庞伟、孙远哲、王欣荻、张龙、张名芳、刘江

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北方工业大学城市道路交通智能控制技术北京市重点实验室,北京 100144

北京交通大学电子信息工程学院,北京 100091

智能交通 道路交通安全 智能网联车辆 车辆队列行驶 车辆换道切入 深度强化学习 队列稳定性

国家重点研发计划北京市自然科学基金

2022YFB43004004212034

2024

10.16058/j.issn.1005-0930.2024.03.003

应用基础与工程科学学报
中国自然资源学会

应用基础与工程科学学报

CSTPCD北大核心
影响因子：0.895
ISSN：1005-0930
年,卷(期)：2024.32(3)
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